A method and apparatus for improving the quality of an attention-based sequence-to-sequence model. The method includes determining an output sequence corresponding to an input sequence based on an attention-based sequence-to-sequence model, selecting at least one target attention head from among a plurality of attention heads, detecting at least one error output token among output tokens constituting the output sequence based on the target attention head, and correcting the output sequence based on the error output token.
Legal claims defining the scope of protection. Each claim is shown in both the original legal language and a plain English translation.
2. The method of claim 1, wherein the selecting comprises selecting, as the target attention head, an attention head generating a predetermined attention weight matrix trained to be a target attention weight matrix corresponding to the target attention head.
This invention relates to machine learning, specifically to attention mechanisms in neural networks, addressing the challenge of efficiently selecting optimal attention heads in transformer-based models. The method involves a process for identifying and utilizing a target attention head within a neural network architecture. The target attention head is chosen based on its ability to generate a predetermined attention weight matrix that has been trained to match a target attention weight matrix. This selection ensures that the chosen attention head aligns with desired attention patterns, improving model performance by focusing on relevant features. The method leverages pre-trained attention heads, allowing for efficient adaptation without extensive retraining. By selecting attention heads that produce specific attention weight matrices, the system enhances interpretability and task-specific performance. The approach is particularly useful in applications requiring precise attention mechanisms, such as natural language processing or computer vision tasks, where targeted attention improves accuracy and efficiency. The invention optimizes neural network performance by dynamically selecting attention heads that best match predefined attention patterns, ensuring robust and adaptable model behavior.
3. The method of claim 2, wherein the predetermined attention weight matrix is trained based on a guide weight matrix having a predetermined shape.
The invention relates to machine learning systems, specifically improving attention mechanisms in neural networks. Attention mechanisms help models focus on relevant parts of input data, but their performance depends on how attention weights are calculated. The problem addressed is the inefficiency of traditional attention mechanisms, which often require extensive training or lack interpretability. The invention describes a method for training an attention weight matrix using a guide weight matrix with a predetermined shape. The guide weight matrix provides a structured prior that influences the training of the attention weights, ensuring they align with desired patterns or constraints. This approach improves efficiency by reducing the need for extensive training while maintaining or enhancing model performance. The guide weight matrix can enforce sparsity, symmetry, or other structural properties, making the attention mechanism more interpretable and controllable. The method involves initializing the attention weight matrix based on the guide weight matrix and then refining it through training. The guide weight matrix acts as a regularizer, guiding the optimization process toward solutions that adhere to the predefined structure. This technique is particularly useful in applications where interpretability, efficiency, or specific attention patterns are critical, such as natural language processing, computer vision, or reinforcement learning. The invention ensures that the trained attention mechanism remains aligned with the intended structure while adapting to the data.
4. The method of claim 3, wherein the guide weight matrix is determined based on any one or any combination of an output sequence length, an input frame length, a start shift, an end shift, and a diffusion ratio.
This invention relates to a method for determining a guide weight matrix used in processing sequences, such as audio or signal data. The method addresses the challenge of efficiently aligning and transforming input sequences into desired output sequences, particularly in applications like speech synthesis, audio processing, or time-series analysis. The guide weight matrix is a key component that helps map input frames to output frames, ensuring accurate and smooth transitions between them. The method calculates the guide weight matrix based on one or more of the following parameters: output sequence length, input frame length, start shift, end shift, and diffusion ratio. The output sequence length defines the duration or number of frames in the final processed sequence. The input frame length specifies the duration or number of frames in the original input sequence. The start shift and end shift adjust the alignment between the input and output sequences, allowing for flexible positioning of the output relative to the input. The diffusion ratio controls the spread or distribution of the input frames across the output sequence, ensuring smooth transitions and avoiding abrupt changes. By dynamically adjusting these parameters, the method enables precise control over the transformation process, improving the quality and accuracy of the output sequence. This approach is particularly useful in applications where maintaining temporal coherence and smooth transitions between input and output sequences is critical. The method can be applied in various domains, including but not limited to speech synthesis, audio processing, and time-series analysis.
5. The method of claim 2, wherein the predetermined attention weight matrix is trained to have a different distribution of attention weights for each step.
This invention relates to machine learning, specifically to improving attention mechanisms in neural networks. The problem addressed is the limitation of conventional attention mechanisms that apply uniform attention weights across all processing steps, which can reduce model performance in tasks requiring dynamic focus on different parts of input data. The invention describes a method for training an attention weight matrix to have a distinct distribution of attention weights for each processing step. This means that at each step, the model learns to allocate attention differently, allowing it to adaptively emphasize or suppress different parts of the input data as needed. The method involves training the attention mechanism to generate step-specific attention patterns, which can improve the model's ability to capture hierarchical or sequential dependencies in the data. The attention mechanism is part of a neural network that processes input data, such as sequences of tokens in natural language processing or pixels in image recognition. The trained attention weight matrix dynamically adjusts attention weights based on the step, enabling the model to focus on relevant information at each stage of processing. This approach enhances the model's interpretability and performance by allowing it to prioritize different features or elements of the input data at different stages. The invention is particularly useful in tasks where the importance of input elements varies across processing steps, such as machine translation, speech recognition, or time-series analysis. By training the attention mechanism to adapt its focus dynamically, the model can achieve better accuracy and efficiency in capturing complex patterns in the data.
6. The method of claim 2, wherein the predetermined attention weight matrix is trained to determine an attention weight of a current step based on a cumulative sum of attention weights of previous steps.
This invention relates to machine learning, specifically to attention mechanisms in neural networks. The problem addressed is improving the efficiency and accuracy of attention-based models by dynamically adjusting attention weights based on prior computational steps. Traditional attention mechanisms often compute weights independently at each step, which can lead to inefficiencies and suboptimal performance. The invention introduces a method where a predetermined attention weight matrix is trained to calculate the attention weight for a current step by incorporating the cumulative sum of attention weights from previous steps. This approach leverages historical attention data to refine current weight calculations, enhancing model performance. The method involves training the attention weight matrix to learn how past attention weights influence future steps, allowing the model to adapt dynamically. This cumulative weighting strategy improves the model's ability to focus on relevant information over time, leading to better accuracy and efficiency in tasks such as natural language processing, computer vision, and other attention-based applications. The invention ensures that the attention mechanism evolves with each step, avoiding static weight assignments and improving overall model adaptability.
7. The method of claim 1, wherein the selecting comprises selecting, as the target attention head, an attention head generating an attention weight matrix most suitable for a predetermined purpose.
The invention relates to machine learning, specifically to improving the performance of transformer-based models by optimizing attention mechanisms. The problem addressed is the inefficiency of conventional attention mechanisms in transformer models, which often apply all attention heads uniformly without considering their suitability for specific tasks. This can lead to suboptimal performance, as not all attention heads contribute equally to the desired outcome. The solution involves a method for selecting a target attention head from multiple attention heads in a transformer model. The selection process identifies the attention head that generates an attention weight matrix most suitable for a predetermined purpose, such as improving accuracy, reducing computational cost, or enhancing interpretability. The predetermined purpose is defined based on the specific requirements of the task, such as optimizing for accuracy in natural language processing or reducing latency in real-time applications. The method evaluates the performance of each attention head by analyzing the attention weight matrices they produce. The most suitable attention head is then selected based on predefined criteria aligned with the predetermined purpose. This targeted selection allows the model to focus computational resources on the most effective attention mechanisms, improving efficiency and performance. The approach can be applied during training or inference to dynamically adapt the model's attention mechanisms to the task at hand.
8. The method of claim 1, wherein the selecting comprises selecting the target attention head based on a guide weight matrix having a predetermined shape according to a predetermined purpose.
This invention relates to machine learning, specifically to attention mechanisms in neural networks, addressing the challenge of efficiently selecting attention heads in transformer-based models. The method involves dynamically selecting a target attention head from multiple available attention heads in a neural network layer. The selection process is guided by a guide weight matrix, which has a predetermined shape and is designed for a specific purpose, such as improving computational efficiency, enhancing model performance, or optimizing resource allocation. The guide weight matrix influences the selection by providing structured weights that prioritize certain attention heads over others based on predefined criteria. This approach allows the model to adaptively focus on the most relevant attention heads, reducing redundancy and improving overall efficiency. The method can be applied in various transformer architectures, including those used in natural language processing, computer vision, and other domains where attention mechanisms are employed. By leveraging the guide weight matrix, the system ensures that the selected attention heads align with the intended purpose, whether it be speed, accuracy, or resource optimization. This technique enhances the flexibility and adaptability of transformer models in different applications.
9. The method of claim 1, wherein the selecting comprises selecting the target attention head by performing monotonic regression analysis on attention weight matrices generated by the plurality of attention heads, in response to the attention-based sequence-to-sequence model having monotonic properties.
This invention relates to improving attention-based sequence-to-sequence models, particularly in scenarios where the model exhibits monotonic properties. The core challenge addressed is efficiently selecting the most relevant attention head from multiple attention heads in such models to enhance performance and interpretability. The method involves analyzing attention weight matrices produced by the attention heads. Monotonic regression analysis is applied to these matrices to identify and select the target attention head. This selection process leverages the inherent monotonic properties of the sequence-to-sequence model, ensuring that the chosen attention head aligns with the model's behavior. The approach improves the model's ability to focus on relevant input sequences, leading to better accuracy and efficiency in tasks like machine translation, text summarization, or speech recognition. By incorporating monotonic regression, the method avoids arbitrary or suboptimal attention head selection, ensuring that the model's attention mechanism remains consistent with its underlying structure. This technique is particularly useful in applications where sequence order and alignment are critical, such as time-series forecasting or structured data processing. The invention enhances the interpretability and reliability of attention-based models in real-world applications.
10. The method of claim 1, wherein the selecting comprises selecting the target attention head based on entropy of attention weight matrices generated by the plurality of attention heads.
The invention relates to improving the performance of attention mechanisms in neural networks, particularly in transformer-based models. The problem addressed is the inefficiency of using all attention heads in a multi-head attention mechanism, which can lead to computational redundancy and suboptimal performance. The solution involves dynamically selecting a subset of attention heads based on their entropy, which measures the diversity or uncertainty in their attention weight distributions. The method involves generating attention weight matrices for each of the plurality of attention heads in a multi-head attention mechanism. The entropy of these matrices is then calculated to quantify the informativeness or diversity of each head's attention patterns. Attention heads with higher entropy are selected as target attention heads, as they are deemed more informative or critical for the task. This selection process reduces computational overhead by focusing on the most relevant attention heads while maintaining or improving model performance. The approach can be applied during both training and inference phases of a neural network. By adaptively selecting attention heads based on entropy, the method optimizes resource usage and enhances the efficiency of attention mechanisms in tasks such as natural language processing, computer vision, or other domains where transformers are employed. The solution is particularly useful in scenarios where computational efficiency is critical, such as edge devices or real-time applications.
11. The method of claim 10, wherein the selecting of the target attention head based on the entropy comprises selecting, as the target attention head, an attention head generating an attention weight matrix having the largest entropy from among the attention weight matrices.
This invention relates to optimizing attention mechanisms in neural networks, particularly in transformer-based models, to improve computational efficiency and performance. The problem addressed is the high computational cost of attention mechanisms, which often involve processing all attention heads equally, even when some contribute less to the model's output. The solution involves dynamically selecting attention heads based on entropy to prioritize those that provide the most informative or diverse attention patterns. The method involves calculating entropy for each attention weight matrix generated by the attention heads in a transformer model. Entropy measures the uncertainty or diversity in the attention distribution, with higher entropy indicating more varied or informative attention patterns. The attention head producing the highest entropy matrix is selected as the target attention head, meaning it is prioritized for further processing or retained while others may be pruned or deactivated. This selective approach reduces computational overhead by focusing resources on the most impactful attention heads, improving efficiency without significantly degrading model performance. The technique can be applied during inference or training to adaptively optimize the model's attention mechanism based on real-time data.
12. The method of claim 10, wherein the selecting of the target attention head based on the entropy comprises selecting the target attention head based on a Kullback-Leibler divergence.
The invention relates to machine learning, specifically to improving attention mechanisms in neural networks by selecting target attention heads based on entropy metrics. Attention mechanisms in transformers and similar models distribute focus across input elements, but inefficient attention allocation can degrade performance. The invention addresses this by dynamically selecting attention heads using entropy-based metrics, such as Kullback-Leibler divergence, to optimize information flow and computational efficiency. The method involves calculating entropy or divergence metrics for attention heads to identify which heads contribute most to model performance. Kullback-Leibler divergence measures the difference between probability distributions, helping determine how much information one attention head provides over another. By selecting attention heads with higher entropy or divergence values, the model can prioritize heads that capture more meaningful patterns, reducing redundancy and improving efficiency. This approach can be applied during training or inference to adaptively refine attention mechanisms, enhancing accuracy and reducing computational overhead. The technique is particularly useful in large-scale models where attention mechanisms dominate resource usage.
13. The method of claim 1, wherein the selecting comprises selecting, as the target attention head, an attention head generating an attention weight matrix having a largest distance between distributions of rows therein.
The invention relates to machine learning, specifically to attention mechanisms in neural networks, addressing the challenge of improving model performance by dynamically selecting optimal attention heads. Attention mechanisms distribute focus across input data, but existing models often use fixed or suboptimal attention heads, leading to inefficiencies. The invention improves this by dynamically selecting a target attention head based on the diversity of attention weights. The method involves analyzing attention weight matrices, where each matrix represents how different parts of the input interact. The selection process identifies the attention head whose matrix exhibits the greatest distance between the distributions of its rows. This distance metric quantifies how distinct the attention patterns are across different input elements, ensuring the chosen head captures the most diverse and informative relationships. By prioritizing heads with high row distribution distance, the model can better adapt to varying input contexts, enhancing accuracy and robustness. This approach is particularly useful in transformer-based architectures, where multiple attention heads operate in parallel, and selecting the most informative head can improve downstream tasks like translation, summarization, or classification. The invention thus provides a data-driven method to optimize attention mechanisms without manual tuning, improving model efficiency and performance.
16. The method of claim 1, wherein the correcting comprises excluding the at least one error output token from the output sequence.
This invention relates to error correction in sequence generation systems, such as those used in natural language processing or machine translation. The problem addressed is the presence of incorrect or erroneous tokens in generated output sequences, which can degrade performance and accuracy. The invention provides a method for correcting such errors by excluding erroneous output tokens from the final sequence. The method involves identifying at least one error output token within a generated sequence. Once identified, the erroneous token is removed or excluded from the output sequence, ensuring that only correct tokens are retained. This correction process may be applied iteratively or in real-time during sequence generation to improve output quality. The invention may be used in various applications, including but not limited to, machine translation, text summarization, and speech recognition, where accurate sequence generation is critical. By excluding erroneous tokens, the method enhances the reliability and coherence of the generated output.
17. The method of claim 1, wherein the correcting comprises determining a next input token among other output token candidates other than the at least one error output token.
This invention relates to error correction in natural language processing (NLP) systems, specifically for correcting errors in generated text outputs. The problem addressed is the occurrence of incorrect or nonsensical tokens in generated text, which can degrade the quality and usability of NLP applications such as chatbots, translation systems, or text generation tools. The method involves identifying at least one error output token in a generated sequence of tokens and correcting it by determining a next input token from among other output token candidates. The correction process evaluates alternative tokens to replace the erroneous one, ensuring the output remains coherent and contextually appropriate. The system may use probabilistic models, language models, or other statistical techniques to assess the likelihood of each candidate token, selecting the most plausible replacement based on contextual and linguistic factors. The method may also involve analyzing the surrounding tokens in the sequence to determine the most suitable correction, ensuring grammatical and semantic consistency. Additionally, the system may consider user feedback or historical data to refine the correction process over time. The goal is to improve the accuracy and reliability of text generation systems by dynamically addressing errors in real-time or during post-processing. This approach enhances the performance of NLP applications by minimizing errors and improving the overall quality of generated text.
18. The method of claim 17, further comprising determining an input token of a step in which the at least one error output token is output, to be the next input token.
This invention relates to error correction in sequence-to-sequence models, particularly for handling errors in generated output sequences. The problem addressed is the propagation of errors in iterative decoding processes, where incorrect tokens in an output sequence can lead to further errors in subsequent steps. The solution involves a method for correcting these errors by identifying erroneous output tokens and using them to guide the next input token selection, thereby improving the accuracy of the generated sequence. The method operates by first identifying at least one error output token in a generated sequence. Once these errors are detected, the system determines the input token corresponding to the step where the error occurred. This input token is then selected as the next input token for the model, allowing the system to re-evaluate and correct the erroneous output. This approach helps break the cycle of error propagation by ensuring that the model reconsiders the problematic step with updated context, leading to more accurate subsequent outputs. The method can be applied iteratively to refine the output sequence further, improving overall performance. This technique is particularly useful in applications like machine translation, text generation, and speech recognition, where maintaining sequence integrity is critical.
19. The method of claim 1, wherein the number of attention heads corresponds to a product of the number of attention layers and the number of decoder layers in the attention-based sequence-to-sequence model.
This invention relates to attention-based sequence-to-sequence models, specifically optimizing the number of attention heads in such models. The problem addressed is the computational inefficiency and suboptimal performance that can arise from improperly configured attention mechanisms in transformer-based architectures. The solution involves dynamically determining the number of attention heads based on the model's depth, specifically as a product of the number of attention layers and the number of decoder layers. This ensures that the attention mechanism scales appropriately with the model's complexity, improving both computational efficiency and performance. The approach allows for a balanced distribution of attention resources across layers, preventing over- or under-utilization of attention heads. By linking the number of attention heads to the model's structural parameters, the method ensures that the attention mechanism remains effective regardless of the model's size or depth. This optimization is particularly useful in large-scale sequence-to-sequence tasks, such as machine translation or text generation, where efficient attention allocation is critical for maintaining accuracy and reducing computational overhead. The invention provides a systematic way to configure attention heads without manual tuning, making it suitable for automated model design and deployment.
20. A non-transitory computer-readable storage medium storing instructions that, when executed by a processor, cause the processor to perform the method of claim 1.
A system and method for processing data involves a non-transitory computer-readable storage medium containing executable instructions. When executed by a processor, these instructions cause the processor to perform a method for analyzing and transforming data. The method includes receiving input data, processing the data to extract relevant features, and applying a transformation algorithm to generate an output. The transformation algorithm may involve mathematical operations, statistical analysis, or machine learning techniques to modify the data structure or content. The processed data is then stored or transmitted for further use. The system ensures efficient data handling by optimizing computational resources and minimizing processing time. The method may also include error detection and correction mechanisms to maintain data integrity during processing. The storage medium ensures the instructions are persistently available for repeated execution, enabling reliable and consistent data processing operations. The system is applicable in various domains, including data analytics, machine learning, and information retrieval, where efficient and accurate data transformation is essential.
22. The electronic device of claim 21, wherein the processor is configured to select, as the target attention head, an attention head generating a predetermined attention weight matrix trained to be a target attention weight matrix corresponding to the target attention head.
This invention relates to electronic devices with processors configured to optimize attention mechanisms in neural networks, particularly for tasks requiring selective focus on specific data elements. The problem addressed is the inefficiency in traditional attention mechanisms that uniformly distribute attention across all elements, leading to suboptimal performance in tasks where certain elements are more relevant than others. The electronic device includes a processor that selects a target attention head from multiple attention heads in a neural network. The target attention head is chosen based on its ability to generate a predetermined attention weight matrix that has been trained to match a target attention weight matrix. This target matrix represents the ideal distribution of attention weights for the specific task, ensuring that the selected attention head focuses on the most relevant data elements. The processor evaluates the attention heads by comparing their generated matrices to the target matrix, selecting the one with the closest match. This selection process enhances the neural network's performance by aligning attention distribution with task-specific requirements, improving accuracy and efficiency in applications such as natural language processing, computer vision, and other machine learning tasks. The invention ensures that the attention mechanism adapts dynamically to prioritize critical information, addressing the limitations of static or uniformly distributed attention models.
23. The electronic device of claim 22, wherein the predetermined attention weight matrix is trained based on a guide weight matrix having a predetermined shape.
The invention relates to electronic devices configured to process data using neural networks, specifically focusing on attention mechanisms. The problem addressed is improving the efficiency and accuracy of attention-based models by optimizing the training of attention weight matrices. Traditional attention mechanisms often rely on learned weights, which can be computationally expensive and may not generalize well across different tasks. The electronic device includes a neural network with an attention mechanism that uses a predetermined attention weight matrix. This matrix is trained based on a guide weight matrix, which has a predefined shape. The guide weight matrix provides a structured initialization or regularization for the attention weights, ensuring that the learned attention weights adhere to a specific pattern or constraint. This approach helps in reducing training time, improving convergence, and enhancing the model's performance on specific tasks by leveraging prior knowledge embedded in the guide weight matrix. The guide weight matrix can be designed to enforce sparsity, symmetry, or other desirable properties in the attention weights, making the model more interpretable and efficient. The predetermined shape of the guide weight matrix can be based on domain-specific knowledge or empirical observations, ensuring that the attention mechanism aligns with the underlying data structure. This method is particularly useful in applications where interpretability and computational efficiency are critical, such as natural language processing, computer vision, and reinforcement learning.
24. The electronic device of claim 22, wherein the predetermined attention weight matrix is trained to have a different distribution of attention weights for each step.
This invention relates to electronic devices implementing neural networks, specifically those using attention mechanisms to process sequential data. The problem addressed is the inefficiency of conventional attention mechanisms that apply uniform attention weights across all processing steps, leading to suboptimal performance in tasks requiring dynamic focus on different parts of input data. The electronic device includes a neural network with an attention mechanism that employs a predetermined attention weight matrix. Unlike traditional approaches, this matrix is trained to have a distinct distribution of attention weights for each processing step. This allows the device to dynamically adjust its focus on different input elements at different stages, improving accuracy and efficiency in tasks like natural language processing, time-series analysis, or image recognition. The attention weight matrix is pre-trained, meaning it is optimized before deployment to ensure optimal weight distributions for each step. This pre-training step involves learning from labeled data to determine the most effective attention patterns for the specific application. The device then applies this pre-trained matrix during inference, enabling real-time processing with enhanced performance. By varying attention weights per step, the device can prioritize relevant input elements dynamically, leading to better handling of long-range dependencies and contextual relationships in sequential data. This approach is particularly useful in applications where input data varies in importance across different processing stages, such as machine translation, speech recognition, or autonomous decision-making systems. The invention improves over prior art by providing a more adaptive and efficient attention
25. The electronic device of claim 21, wherein the processor is configured to select, as the target attention head, an attention head generating an attention weight matrix most suitable for a predetermined purpose.
The invention relates to electronic devices with neural network processing capabilities, specifically focusing on optimizing attention mechanisms in transformer-based models. The problem addressed is the inefficiency in selecting attention heads within multi-head attention architectures, where different attention heads may generate varying attention weight matrices that are more or less suitable for specific tasks. The solution involves a processor in an electronic device that dynamically selects a target attention head based on its ability to generate an attention weight matrix most suitable for a predetermined purpose. This selection process ensures that the most relevant attention head is used, improving computational efficiency and performance for tasks such as natural language processing, image recognition, or other applications requiring transformer models. The processor evaluates the attention weight matrices produced by each attention head and chooses the one that best aligns with the desired outcome, such as maximizing accuracy, minimizing latency, or optimizing resource usage. This approach enhances the adaptability and effectiveness of neural network models in electronic devices by tailoring attention mechanisms to specific operational needs.
26. The electronic device of claim 21, wherein the processor is configured to select the target attention head based on a guide weight matrix having a predetermined shape according to a predetermined purpose.
The invention relates to electronic devices with processors configured to select a target attention head in neural network models, particularly for optimizing attention mechanisms in machine learning tasks. The problem addressed is the inefficiency in selecting attention heads, which can lead to suboptimal performance in tasks requiring focused attention, such as natural language processing or computer vision. The electronic device includes a processor that selects a target attention head based on a guide weight matrix. This matrix has a predetermined shape and is designed for a specific purpose, such as enhancing interpretability, improving computational efficiency, or focusing on relevant features. The guide weight matrix acts as a structured constraint, ensuring the attention mechanism aligns with the intended function of the neural network. The processor uses this matrix to guide the selection process, improving the model's ability to prioritize relevant information while reducing unnecessary computations. The invention also involves a neural network model with multiple attention heads, where each head processes different aspects of input data. The guide weight matrix is pre-defined to match the desired behavior of the attention mechanism, such as emphasizing certain input features or reducing redundancy. By integrating this matrix into the selection process, the device ensures that the chosen attention head aligns with the model's objectives, leading to more efficient and accurate predictions. This approach enhances the adaptability and performance of neural networks in various applications.
27. The electronic device of claim 21, wherein the processor is configured to select the target attention head by performing monotonic regression analysis on attention weight matrices generated by the plurality of attention heads, in response to the attention-based sequence-to-sequence model having monotonic properties.
This invention relates to electronic devices implementing attention-based sequence-to-sequence models, particularly for tasks where the model exhibits monotonic properties. The problem addressed is efficiently selecting an optimal attention head from multiple attention heads in such models to improve performance and accuracy. The electronic device includes a processor configured to analyze attention weight matrices generated by the attention heads. The processor performs monotonic regression analysis on these matrices to identify the most suitable attention head, referred to as the target attention head. Monotonic regression is used because the sequence-to-sequence model has inherent monotonic properties, meaning the attention weights follow a predictable, non-decreasing or non-increasing pattern over time. By leveraging this property, the processor can more accurately determine which attention head best captures the relevant dependencies between input and output sequences. The attention-based sequence-to-sequence model processes input sequences and generates corresponding output sequences, with each attention head contributing to the alignment between input and output elements. The monotonic regression analysis helps filter out noisy or less relevant attention heads, ensuring the selected target attention head provides the most reliable alignment. This approach enhances the model's efficiency and accuracy, particularly in tasks where sequential dependencies are structured in a monotonic manner, such as machine translation or speech recognition.
28. The electronic device of claim 21, wherein the processor is configured to select the target attention head based on entropy of attention weight matrices generated by the plurality of attention heads.
The invention relates to optimizing neural network models, specifically transformer-based architectures, by dynamically selecting attention heads to improve computational efficiency and performance. The problem addressed is the high computational cost and redundancy in multi-head attention mechanisms, where multiple attention heads may produce similar or redundant outputs, leading to inefficiency. The electronic device includes a processor configured to analyze attention weight matrices generated by multiple attention heads in a transformer model. The processor calculates the entropy of these matrices to quantify the diversity of attention patterns. Attention heads with lower entropy values are deemed less informative, as they exhibit more uniform or redundant attention distributions. The processor then selects a subset of attention heads, referred to as target attention heads, based on higher entropy values, indicating more diverse and informative attention patterns. This selection process reduces computational overhead by focusing on the most relevant attention heads while maintaining model performance. The invention further includes a memory storing the attention weight matrices and a display for visualizing the selected attention heads. The dynamic selection of attention heads allows for adaptive model optimization, improving efficiency without sacrificing accuracy. This approach is particularly useful in applications requiring real-time processing, such as natural language processing and computer vision tasks.
29. The electronic device of claim 28, wherein the processor is configured to select, as the target attention head, an attention head generating an attention weight matrix having the largest entropy from among the attention weight matrices.
The invention relates to electronic devices with processors configured to optimize attention mechanisms in neural networks, particularly for selecting attention heads based on entropy. Attention mechanisms in neural networks distribute focus across different parts of input data, but existing methods may not efficiently identify the most informative attention heads. The invention addresses this by selecting a target attention head that generates an attention weight matrix with the highest entropy, indicating greater diversity and informativeness in the attention distribution. The processor evaluates multiple attention heads and their corresponding attention weight matrices, calculating entropy for each to determine which head provides the most balanced and informative attention distribution. This selection process enhances the neural network's ability to capture relevant features from input data, improving performance in tasks like natural language processing or computer vision. The invention builds on a system where the processor already processes input data through multiple attention heads, each generating an attention weight matrix. By prioritizing the head with the highest entropy, the system ensures that the most diverse and informative attention patterns are used, leading to better model accuracy and efficiency. This approach is particularly useful in large-scale neural networks where attention mechanisms play a critical role in performance.
30. The electronic device of claim 21, wherein the processor is configured to select, as the target attention head, an attention head generating an attention weight matrix having a largest distance between distributions of rows therein.
The invention relates to electronic devices with neural network-based attention mechanisms, particularly for improving the performance of transformer models by selecting an optimal attention head. The problem addressed is the inefficiency in transformer models where multiple attention heads may redundantly process similar information, leading to suboptimal computational efficiency and performance. The solution involves dynamically selecting a target attention head based on the diversity of attention weights generated by each head. Specifically, the processor in the electronic device evaluates the attention weight matrices produced by different attention heads and selects the head whose matrix exhibits the greatest distance between the distributions of its rows. This selection criterion ensures that the chosen attention head captures the most diverse and informative attention patterns, enhancing the model's ability to focus on relevant input features. The processor may further adjust the attention weights of the selected head to improve performance, such as by applying a sparsification technique to reduce computational overhead. This approach optimizes the transformer model's efficiency by leveraging the most informative attention head while minimizing redundant computations.
31. The electronic device of claim 21, wherein the processor is configured to detect at least one error attention weight in which differences between attention weights of the target attention head and a guide weight matrix are greater than or equal to a threshold value, among attention weights between the input sequence and the output sequence of the target attention head, and determine an output token corresponding to the at least one error attention weight to be the at least one error output token.
This invention relates to error detection in neural network-based sequence-to-sequence models, particularly in transformer architectures. The problem addressed is identifying erroneous output tokens generated by attention mechanisms in these models, which can degrade performance in tasks like machine translation or text generation. The system includes an electronic device with a processor that analyzes attention weights in a transformer model. Specifically, it compares attention weights from a target attention head against a predefined guide weight matrix. The processor detects error attention weights where the differences between these weights and the guide matrix exceed a threshold. These error attention weights indicate potential errors in the model's output. The corresponding output tokens linked to these error attention weights are flagged as error output tokens, allowing for correction or further processing. The guide weight matrix serves as a reference, representing expected or correct attention patterns. By comparing actual attention weights to this reference, the system identifies deviations that correlate with incorrect output tokens. This approach improves model reliability by pinpointing specific errors in the sequence generation process. The method is applicable to any transformer-based model where attention weight analysis can reveal output inaccuracies.
32. The electronic device of claim 21, wherein the processor is configured to detect at least one error attention weight in which a similarity to an attention weight of a previous step is greater than or equal to a threshold value, among attention weights between the input sequence and the output sequence of the target attention head, and determine an output token corresponding to the at least one error attention weight to be the at least one error output token.
This invention relates to error detection in neural network-based sequence-to-sequence models, particularly those using attention mechanisms. The problem addressed is identifying erroneous output tokens generated by such models, which can occur due to misalignment between input and output sequences. The solution involves analyzing attention weights to detect anomalies that indicate potential errors in the model's output. The system includes an electronic device with a processor configured to process input and output sequences using a target attention head. The processor calculates attention weights between the input and output sequences, which represent the model's focus on different parts of the input when generating each output token. To detect errors, the processor identifies attention weights where the similarity to an attention weight from a previous step exceeds a predefined threshold. These are classified as error attention weights. The corresponding output tokens are then marked as error output tokens, indicating potential inaccuracies in the model's predictions. This approach leverages the attention mechanism's internal state to improve error detection without requiring external validation data. The method enhances the reliability of sequence-to-sequence models by flagging outputs that may be unreliable due to attention misalignment.
33. The electronic device of claim 21, wherein the processor is configured to exclude the at least one error output token from the output sequence.
The invention relates to electronic devices with natural language processing capabilities, specifically addressing errors in generated text outputs. The device includes a processor that processes input data to generate an output sequence, such as text, using a language model. During this process, the processor identifies error output tokens—tokens that do not meet predefined accuracy or relevance criteria. To improve output quality, the processor excludes these error tokens from the final output sequence, ensuring only valid tokens are presented. This exclusion mechanism may involve filtering, masking, or omitting the erroneous tokens based on confidence scores, context analysis, or other evaluation metrics. The device may further include memory for storing the language model and input/output interfaces for receiving and transmitting data. The exclusion process enhances the reliability and coherence of generated text, particularly in applications like machine translation, chatbots, or text generation systems where accuracy is critical. The invention focuses on dynamically refining output sequences by removing detected errors, improving the overall performance of language processing tasks.
34. The electronic device of claim 21, wherein the processor is configured to determine a next input token among other output token candidates other than the at least one error output token.
The invention relates to electronic devices with natural language processing capabilities, specifically addressing errors in token generation during text prediction or generation tasks. The problem being solved is the handling of incorrect or erroneous tokens produced by a language model, which can degrade the quality of generated text. The device includes a processor that processes input data to generate output tokens, where some tokens may be erroneous. The processor is configured to identify at least one error output token and then determine a next input token from other output token candidates, excluding the identified error tokens. This ensures that subsequent predictions are based on valid tokens, improving the accuracy and coherence of the generated text. The processor may also filter or correct erroneous tokens before proceeding with further token generation. The invention enhances the reliability of language models by dynamically adjusting the token selection process to avoid propagating errors. This is particularly useful in applications like autocomplete, chatbots, and machine translation where maintaining context and accuracy is critical. The system may also include memory for storing token sequences and a display for presenting the generated text. The overall goal is to improve the robustness of language model outputs by intelligently handling and excluding erroneous tokens during the generation process.
35. The electronic device of claim 34, wherein the processor is configured to determine an input token of a step in which the at least one error output token is output, to be the next input token.
The invention relates to error handling in electronic devices, particularly for systems processing sequences of tokens, such as natural language processing or machine learning models. The problem addressed is improving error recovery in token-based systems where errors in output tokens can disrupt subsequent processing steps. The invention provides a method for handling such errors by dynamically adjusting input tokens to correct or mitigate the impact of erroneous outputs. The electronic device includes a processor configured to process input tokens sequentially, generating output tokens. When an error is detected in an output token, the processor identifies the corresponding input token that led to the erroneous output. Instead of proceeding with the next input token in the sequence, the processor selects the input token associated with the erroneous step as the next input token. This allows the system to re-evaluate or correct the erroneous step before continuing, improving accuracy and robustness in token-based processing tasks. The processor may also adjust subsequent processing steps based on the corrected input token to maintain consistency in the output sequence. This approach is particularly useful in applications like speech recognition, machine translation, or code generation, where errors in intermediate steps can compound over time.
37. The electronic device of claim 36, wherein the encoder and the decoder are included in an artificial neural network.
The invention relates to electronic devices incorporating artificial neural networks for encoding and decoding data. The device includes an encoder and a decoder, both integrated within an artificial neural network, to process input data. The encoder transforms the input data into a compressed or latent representation, while the decoder reconstructs the original data from this representation. This architecture enables efficient data compression, feature extraction, or generative tasks by leveraging the neural network's ability to learn complex patterns. The encoder and decoder may be trained jointly or separately, depending on the application. This approach is particularly useful in applications like image processing, natural language processing, or signal reconstruction, where preserving data integrity while reducing dimensionality is critical. The neural network may be configured as an autoencoder, variational autoencoder, or other architectures, depending on the specific requirements of the task. The integration of encoding and decoding within a neural network allows for end-to-end learning, improving performance and adaptability compared to traditional methods.
38. The electronic device of claim 36, wherein the one or more processors are configured to select the target attention head from among a plurality of attention weight matrices stored in the attention-based sequence-to-sequence model.
This invention relates to attention-based sequence-to-sequence models used in machine learning, particularly for improving the efficiency and accuracy of attention mechanisms in neural networks. The problem addressed is the computational overhead and inefficiency in selecting attention heads, which are critical components in models like transformers that process sequential data. Traditional approaches often require evaluating all attention heads, leading to unnecessary computations and reduced performance. The invention describes an electronic device with one or more processors configured to optimize attention head selection in a sequence-to-sequence model. The processors select a target attention head from a plurality of attention weight matrices stored in the model. This selection process is designed to enhance computational efficiency by avoiding the need to evaluate all possible attention heads, thereby reducing the model's resource consumption while maintaining or improving performance. The attention weight matrices represent learned relationships between input and output sequences, and the selection mechanism dynamically chooses the most relevant attention head based on the input data. This approach is particularly useful in applications requiring real-time processing, such as natural language processing, speech recognition, and machine translation, where computational efficiency is critical. The invention improves upon prior art by introducing a more adaptive and selective attention mechanism, reducing redundancy and improving scalability.
40. The electronic device of claim 36, wherein the one or more processors are configured to train a specific attention head, from among the plurality of attention heads, to generate an attention weight matrix of a desired shape and to select the specific attention head as the target attention head.
This invention relates to machine learning systems, specifically neural networks with attention mechanisms, addressing the challenge of efficiently training and selecting attention heads to improve model performance. Attention mechanisms in neural networks distribute focus across input data, but training multiple attention heads can be computationally expensive and may lead to redundant or suboptimal attention patterns. The invention provides a method to train a specific attention head within a neural network to generate an attention weight matrix of a desired shape, such as a sparse or structured matrix, which can reduce computational overhead and enhance interpretability. The trained attention head is then selected as the target attention head for subsequent processing, allowing the model to focus on relevant input features while discarding or deemphasizing irrelevant ones. This approach improves efficiency by avoiding the need to train all attention heads simultaneously and ensures that the selected attention head aligns with the desired attention pattern, leading to better model accuracy and performance. The invention is applicable to various neural network architectures, including transformers, where attention mechanisms play a critical role in tasks such as natural language processing, computer vision, and speech recognition.
Cooperative Patent Classification codes for this invention. Click any code to explore related patents in that topic.
December 2, 2020
May 14, 2024
Browse 5M+ US patents with plain-English claim translations and AI-generated analysis.